We are interested in understanding the principles underlying the complex adaptive behavior of organisms. Starting with quantitative observations of animal behavior, we aim to integrate quantitative cellular and systems level experimental analysis of underlying neural mechanisms with theoretical, ecological and evolutionary contexts. Rats and mice provide flexible animal models that allow us to monitor and manipulate neural circuits using electrophysiological, optical and molecular techniques. We have made progress using highly-controlled studies of a simple learned odor-cued decision task and are extending our focus toward more complex behaviors. Projects in the lab are wide-ranging and continually evolving. Current topics include (i) olfactory sensory decision-making, (ii) the function of the serotonin system, (iii) the role of uncertainty in brain function and behavior.

Optogenetic identification and control of serotonin neurons in behaving animals

Serotonin is an important neurotransmitter implicated in a wide variety of physiological functions and pathophysiologies but whose function is not well understood. Critically, very little is known about the activity of serotonin-releasing neurons in the brain. This problem is greatly exacerbated by the difficulty in their identification during physiological recordings. To address these problems we are using a combination of behavioral analysis, electrophysiological recording and optical-genetic probes targeted through specific promoters to this class of cells. By selectively activating serotonin neurons with light delivered through implanted fiber optics, we will be able to positively identify them during recordings and to specifically activate them, allowing us to test specific hypotheses concerning the role of serotonin in brain function and behavior.

Funding:
ERC

Olfactory objects and decisions: From psychophysics to neural computation

Object recognition is an important and difficult problem solved by the nervous system. Although visual recognition is far more familiar to us, it is through the chemical senses that object recognition occurs for most organisms. Neural computations within the olfactory system enable faithful recognition and tracking of meaningful odor sources, even when they comprise complex chemical blends embedded in a sea of background odors. The overall aim of this line of work is to understand the neural computations that make olfactory object recognition possible. According to theoretical accounts, object recognition can be understood as a process of probabilistic inference. Under this hypothesis, complex odor stimuli are represented using a probabilistic population code and processed in a Bayesian optimal fashion by the nervous system. To link these normative ideas to specific neurophysiological and behavioral predictions, we are formalizing them using computational models. Experimentally, our main goal is to monitor and perturb object representations in the functioning, computing brain. To this end, we deploy psychophysical tasks in rats which formalize complex real-world olfactory problems and also allow us to operationalize cognitive processes such as attention and memory. By combining such quantitative paradigms with large-scale neural ensemble recordings in the olfactory cortex, we can study how populations of neurons encode and process complex odor scenes, attempt to account for behavioral performance, and test the predictions of theoretical models. At the level of neural circuits and their physiology, we are particularly interested in the origin of neuronal variability, the nature of inter-neuronal correlations, the properties of inter-areal brain communication and the action of neuromodulators.

Evaluating the reliability of knowledge: Neural mechanisms of confidence estimation

Humans and other animals must often make decisions on the basis of imperfect evidence. What is the neural basis for such judgments? How does the brain compute confidence estimates about predictions, memories and judgments? Previously, we found that a population of neurons in the orbitofrontal cortex (OFC) tracks the confidence in decision outcomes. We are seeking to extend these observations by testing whether confidence-related neural activity in the OFC is causally related to confidence judgments. We are also addressing how the uncertainty about a stimulus in the course of decision-making is computed in olfactory sensory cortex. These experiments will give us further insights into the nature of the neural processes underlying confidence estimation.

Colaborators:
Adam Kepecs (CSHL)

Frontal cortex and the control of impulsive action

Inhibition of behaviour is as important as its generation, and failure to inhibit inappropriate actions—impulsivity—is a central feature of pathologies including attention deficit hyperactivity disorder, drug addiction and obsessive compulsive disorder. Previous work has identified the frontal cortex as a central component in the control of inhibiting impulsive actions. The goal of this project is to understand how this brain area performs this function. Two current specific aims are to reveal the activity of frontal cortical neurons while rats are engaged in impulse control task and to examine the effect of inactivating subregions of frontal cortex on impulse control behavior. Recording from large ensembles of neurons in the medial prefrontal cortex (mPFC) and the secondary motor cortex (M2) of rats during performance of the impulse control task allows us to characterize in detail the neural activity in these areas in relationship to behavior. We find that the activity of subpopulations of mPFC and M2 neurons predict the impulse control performance of rats on a trial-by-trial basis. Preliminary results show that reversible inactivation of the mPFC also impairs the ability of rats to inhibit impulsive action. We are now seeking to understand in more detail the nature of the neural representations underlying impulse control.